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 "metadata": {
  "language": "Julia",
  "name": "",
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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Diffusion equation\n",
      "As a first example, I try to solve the diffusion equation, i.e., $$ \\frac {\\partial c} {\\partial t} + \\nabla.(-D \\nabla c)=0$$ The boundary condition is written in the general form of $$a \\nabla \\phi . \\mathbf{n} + b \\phi = c$$ We use Dirichlet boundary condition for the left and right boundaries, i.e., $c=1.0$ at the left boundary and $c=1.0$ at the right boundary. The initial condition is $c=0.0$ at $t=0.$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Analytical solution\n",
      "The anaytical solution of the diffusion problem in a semi-infinite domain is given by $$c=1-\\textrm{erf}(x/2\\sqrt{Dt}).$$ We can choose a long domain to mimic a semi-infinite domain."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Start the solver"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "using JFVM\n",
      "using PyPlot"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Domain size and physical constants"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "D_val = 1.0 # [m^2/s]\n",
      "Lx = 50.0 # [m]\n",
      "c_init = 0.0\n",
      "c_left = 1.0\n",
      "c_right = 0.0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "0.0"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## A diffusion function\n",
      "First, we define a function that accepts a mesh, boundary condition, diffusion coefficient, and final simulation time, and returns a cell value, which is the final concentration profile. I also define a boundary condition function to assign values of one and zero to the left and right boundaries."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "function trans_diff(m::MeshStructure, BC::BoundaryCondition, D_val::Real, t_final::Real)\n",
      "    # a transient diffusion \n",
      "    c_init = 0.0 # initial value of the variable\n",
      "    c_old = createCellVariable(m, 0.0, BC)\n",
      "    D_val = 1.0 # value of the diffusion coefficient\n",
      "    D_cell = createCellVariable(m, D_val) # assigned to cells\n",
      "    # Harmonic average\n",
      "    D_face = harmonicMean(D_cell)\n",
      "    N_steps = 20 # number of time steps\n",
      "    dt= t_final/N_steps # time step\n",
      "    M_diff = diffusionTerm(D_face) # matrix of coefficient for diffusion term\n",
      "    (M_bc, RHS_bc)=boundaryConditionTerm(BC) # matrix of coefficient and RHS for the BC\n",
      "    for i =1:N_steps\n",
      "        (M_t, RHS_t)=transientTerm(c_old, dt, 1.0)\n",
      "        M=M_t-M_diff+M_bc # add all the [sparse] matrices of coefficient\n",
      "        RHS=RHS_bc+RHS_t # add all the RHS's together\n",
      "        c_old = solveLinearPDE(m, M, RHS) # solve the PDE\n",
      "    end\n",
      "    return c_old\n",
      "end\n",
      "\n",
      "function dirich_bc(m::MeshStructure)\n",
      "    BC = createBC(m)\n",
      "    BC.left.a[:]=BC.right.a[:]=0.0\n",
      "    BC.left.b[:]=BC.right.b[:]=1.0\n",
      "    BC.left.c[:]=1.0\n",
      "    BC.right.c[:]=0.0\n",
      "    return BC\n",
      "end"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "dirich_bc (generic function with 1 method)"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Solution\n",
      "I define two domains, one with uniform grids and another with a nonuniform grids with smaller cells near the left boundary. The total number of grids are the same for both case. The figure below shows how you can use grid refinement near the more important areas of the domain to get the best out of your numerical model. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t_final = 5\n",
      "Nx = 11 # number of cells\n",
      "m_uni = createMesh1D(Nx, Lx)\n",
      "BC_uni = dirich_bc(m_uni)\n",
      "c_uni = trans_diff(m_uni, BC_uni, D_val, t_final)\n",
      "x = linspace(0,50,200)\n",
      "m_nonuni = createMesh1D([linspace(0,10,5), linspace(14,50,5)])\n",
      "BC_nonuni = dirich_bc(m_nonuni)\n",
      "c_nonuni = trans_diff(m_nonuni, BC_nonuni, D_val, t_final)\n",
      "c_analytic = 1-erf(x./(2.*sqrt(D_val*t_final)))\n",
      "visualizeCells(c_uni)\n",
      "visualizeCells(c_nonuni)\n",
      "plot(x, c_analytic)\n",
      "legend([\"uniform\", \"nonuniform\", \"analytic\"])\n",
      "axis([0, 10, 0, 1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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",
       "text": [
        "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x7fea7cccf810>)"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "4-element Array{Int64,1}:\n",
        "  0\n",
        " 10\n",
        "  0\n",
        "  1"
       ]
      }
     ],
     "prompt_number": 34
    }
   ],
   "metadata": {}
  }
 ]
}